Biblio

Intelligent reflecting surfaces (IRS) can improve the physical layer security (PLS) by providing a controllable wireless environment. In this paper, we propose a novel PLS technique with the help of IRS implemented by an intelligent mirror array for the visible light communication (VLC) system. First, for the IRS aided VLC system containing an access point (AP), a legitimate user and an eavesdropper, the IRS channel gain and a lower bound of the achievable secrecy rate are derived. Further, to enhance the IRS channel gain of the legitimate user while restricting the IRS channel gain of the eavesdropper, we formulate an achievable secrecy rate maximization problem for the proposed IRS-aided PLS technique to find the optimal orientations of mirrors. Since the sensitivity of mirrors’ orientations on the IRS channel gain makes the optimization problem hard to solve, we transform the original problem into a reflected spot position optimization problem and solve it by a particle swarm optimization (PSO) algorithm. Our simulation results show that secrecy performance can be significantly improved by adding an IRS in a VLC system.

This article presents a practical approach for secure key exchange exploiting reciprocity in wireless transmission. The method relies on the reciprocal channel phase to mask points of a Phase Shift Keying (PSK) constellation. Masking is achieved by adding (modulo 2π) the measured reciprocal channel phase to the PSK constellation points carrying some of the key bits. As the channel phase is uniformly distributed in [0, 2π], knowing the sum of the two phases does not disclose any information about any of its two components. To enlarge the key size over a static or slow fading channel, the Radio Frequency (RF) propagation path is perturbed to create independent realizations of multi-path fading. Prior techniques have relied on quantizing the reciprocal channel state measured at the two ends and thereby suffer from information leakage in the process of key consolidation (ensuring the two ends have access to the same key). The proposed method does not suffer from such shortcomings as raw key bits can be equipped with Forward Error Correction (FEC) without affecting the masking (zero information leakage) property. To eavesdrop a phase value shared in this manner, the Eavesdropper (Eve) would require to solve a system of linear equations defined over angles, each equation corresponding to a possible measurement by the Eve. Channel perturbation is performed such that each new channel state creates an independent channel realization for the legitimate nodes, as well as for each of Eves antennas. As a result, regardless of the Eves Signal-to-Noise Ratio (SNR) and number of antennas, Eve will always face an under-determined system of equations. On the other hand, trying to solve any such under-determined system of linear equations in terms of an unknown phase will not reveal any useful information about the actual answer, meaning that the distribution of the answer remains uniform in [0, 2π].

Physical Unclonable Functions (PUFs) have been designed for many security applications such as identification, authentication of devices and key generation, especially for lightweight electronics. Traditional approaches to enhancing security, such as hash functions, may be expensive and resource dependent. However, modelling attacks using machine learning (ML) show the vulnerability of most PUFs. In this paper, a combination of a 32-bit current mirror and 16-bit arbiter PUFs in 65nm CMOS technology is proposed to improve resilience against modelling attacks. Both PUFs are vulnerable to machine learning attacks and we reduce the output prediction rate from 99.2% and 98.8% individually, to 60%.

Moving target defense (MTD) is becoming popular with the advancements in Software Defined Networking (SDN) technologies. With centralized management through SDN, changing the network attributes such as routes to escape from attacks is simple and fast. Yet, the available alternate routes are bounded by the network topology, and a persistent attacker that continuously perform the reconnaissance can extract the whole link-map of the network. To address this issue, we propose to use virtual shadow networks (VSNs) by applying Network Function Virtualization (NFV) abilities to the network in order to deceive attacker with the fake topology information and not reveal the actual network topology and characteristics. We design this approach under a formal framework for Internet Service Provider (ISP) networks and apply it to the recently emerged indirect DDoS attacks, namely Crossfire, for evaluation. The results show that attacker spends more time to figure out the network behavior while the costs on the defender and network operations are negligible until reaching a certain network size.

Identity masking methods have been developed in recent years for use in multiple applications aimed at protecting privacy. There is only limited work, however, targeted at evaluating effectiveness of methods-with only a handful of studies testing identity masking effectiveness for human perceivers. Here, we employed human participants to evaluate identity masking algorithms on video data of drivers, which contains subtle movements of the face and head. We evaluated the effectiveness of the “personalized supervised bilinear regression method for Facial Action Transfer (FAT)” de-identification algorithm. We also evaluated an edge-detection filter, as an alternate “fill-in” method when face tracking failed due to abrupt or fast head motions. Our primary goal was to develop methods for humanbased evaluation of the effectiveness of identity masking. To this end, we designed and conducted two experiments to address the effectiveness of masking in preventing recognition and in preserving action perception. 1- How effective is an identity masking algorithm?We conducted a face recognition experiment and employed Signal Detection Theory (SDT) to measure human accuracy and decision bias. The accuracy results show that both masks (FAT mask and edgedetection) are effective, but that neither completely eliminated recognition. However, the decision bias data suggest that both masks altered the participants' response strategy and made them less likely to affirm identity. 2- How effectively does the algorithm preserve actions? We conducted two experiments on facial behavior annotation. Results showed that masking had a negative effect on annotation accuracy for the majority of actions, with differences across action types. Notably, the FAT mask preserved actions better than the edge-detection mask. To our knowledge, this is the first study to evaluate a deidentification method aimed at preserving facial ac- ions employing human evaluators in a laboratory setting.

Reflection allows a program to examine and even modify itself, but its power can also lead to violations of encapsulation and even security vulnerabilities. The Wyvern language leverages static types for encapsulation and provides security through an object capability model. We present a design for reflection in Wyvern which respects capability safety and type-based encapsulation. This is accomplished through a mirror-based design, with the addition of a mechanism to constrain the visible type of a reflected object. In this way, we ensure that the programmer cannot use reflection to violate basic encapsulation and security guarantees.